We propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile. Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis shows that high-frequency measures are particularly informative of the dynamic quantiles. Finally, an out-of-sample forecast analysis of quantile-based risk measures confirms the merit of the REQ.
